Simulations and Analysis of the Diffusion Coupling Model in Image Segmentation Based on Synchronization of Neural Oscillators

This paper introduces a boundary detector based on mathematical morphology using multi-direction structuring elements to detect the boundaries of an image, and then to make the image segmentation. This paper describes a modified technique based on gray-scale morphological gradient operations for finding the boundaries of an image. The boundaries finding algorithm, which uses a combination of morphological feature detection and a novel directional sensitive procedure to get the boundaries of an image. Its inherent direction sensitivity is better than the others. To improve the quality of the boundary image extracted, the noisy image is filtered by LUM and morphological filters. After boundary detection, disjoint boundaries are connected to form closed contours and result in a number of image regions. Finally, a region growing algorithm is used to produce regions and complete the image segmentation. Some experimental results are provided to show the validity of this method.

Segmentation is a low-level processing aimed at the partition of an image in disjoint regions, each one homogeneous with respect to some properties like intensity, texture, shape, etc. Such a task is needed in many high-level processing and applications in such diverse fields as remote-sensing, medical imaging, video coding, and industrial automation, just to name a few. In this work of thesis, we first deal with the problem of color based image segmentation, that is based on some form of homogeneity in the color/spectral properties of the image, and then move to the more complex task of texture based segmentation, where the aim is to recognize complex structures in the image which are typically non homogeneous in terms of spectral properties. Concerning color based segmentation, we focus on a specific class of Markov Random Field (MRF) probability models, namely the Tree-Structured Markov Random Fields (TS-MRF), recently proposed to provide a hierarchical multiscale description of images. Based on such a model, the unsupervised image segmentation is carried out by means of a sequence of nested class splits, where each class is modeled as a local binary MRF. As a first contribution, we first extend the model to generic tree structures removing the unnecessary binary constraint, and then devise a new TS-MRF unsupervised segmentation technique which improves upon the original algorithm by selecting a better tree structure and eliminating spurious classes. Such results are obtained by using the Mean-Shift procedure to estimate the number of pdf modes at each node, and to obtain a more reliable initial clustering for subsequent MRF optimization. To this end, we devise a new reliable and fast clustering algorithm based on the Mean-Shift technique, which makes use of a variable-bandwidth strategy based on the k-Nearest Neighbors (k-NN) technique, and is implemented with a speed-up strategy that cuts significantly the computational complexity. Experimental results on synthetic data and real life images from the remote sensing domain prove the potential of the proposed method. Turning to texture based segmentation, we present a novel multiscale texture model, and a related algorithm for the unsupervised segmentation of color images. Elementary textures are characterized by their spatial interactions with neighboring regions along selected directions. Such interactions are modeled in turn by means of a set of Markov chains, one for each direction, whose parameters are collected in a feature vector that synthetically describes the texture. Based on the feature vectors, the texture are then recursively merged, giving rise to larger and more complex textures, which appear at different scales of observation: accordingly, the model is named Hierarchical Multiple Markov Chain (H-MMC). The Texture Fragmentation and Reconstruction (TFR) algorithm, addresses the unsupervised segmentation problem based on the H-MMC model. The ``fragmentation'' step allows one to find the elementary textures of the model, while the ``reconstruction'' step defines the hierarchical image segmentation based on a probabilistic measure (texture score) which takes into account both region scale and inter-region interactions. The performance of the proposed method was assessed through the Prague segmentation benchmark, based on mosaics of real natural textures, and also tested on real-world natural images from the Berkeley dataset. Finally, an application of the TFR algorithm for the unsupervised segmentation of multiresolution remotely sensed images is proposed, based on the significant presence of textural content in last generation high-resolution satellite images. The obtained results further assess the potential of the proposed method for such real life applications.

Image segmentation for object to extract data from ultrasound acquired is an essential preprocessing step for the effective diagnosis. Various image segmentation methods have been studied. In this study, interactive image segmentation method by graph cut algorithm is proposed to develop a variety of applications of vascular ultrasound imaging and diagnostics. General imaging and vascular ultrasound imaging segmentation by entering constrain condition such as foreground and background. In the future it will be able to develop new ultrasound diagnostics.

In the field of computer assisted diagnostic, Image segmentation is one of the most important preprocessing steps. In this paper, the watershed algorithm segmented image by choosing different merging parameters, original image will be divided into different scales of image, High-scale image and low-scale image. High-scale image is part of the pulmonary parenchyma without the main vessel in it, low-scale image used to aid region growing tore move the branching vessels from the pulmonary parenchyma. Selected seed point located inside the branch vessels and determined the region growing threshold, with the aid of low-scale image region growing can segment the branch vessel regions .Removed the regions in the high-scale image to get the final segmentation results. In this paper, a large number of CT lung data segmentation experiment. Proven, good segmentation results are achieved.

Scene analysis for robotic watercraft

Leaf wetness duration (LWD) is a critical parameter used to predict plant disease, but its determination under actual field conditions is a major challenge. In this study, a method for determining LWD using thermal infrared imaging was developed and applied to cucumber plants grown in a solar greenhouse. Thermal images of the plant leaves were captured using an infrared scanning camera, and a leaf wetness area segmentation method consisting of two procedures was applied. First, a color space conversion was performed automatically by an image-processing algorithm. Then, the K-means clustering algorithm was applied to enable the segmentation of the wetness area on the thermal image. Subsequently, to enable overall thermal image analysis, an initial leaf wetness threshold (LWT) of 5% was defined (where wetness values higher than 5% indicated that the leaf was in a wet state). The results of comparative experiments conducted using thermal images of plant leaves captured using an infrared scanning camera and human visual observation indicated that the estimated LWD values were generally higher than the observed LWD values, because slight leaf wetness condensations were overlooked by the human eye but detected by the infrared scanning camera. While these differences were not found to be statistically significant in this study, the proposed method for determining LWD using thermal infrared imaging may provide a new LWD detection method for cucumber and other plants grown in solar greenhouses. Keywords: thermal imaging, K-means clustering algorithm, leaf wetness duration, cucumber DOI: 10.25165/j.ijabe.20201303.4301 Citation: Wen D M, Ren A X, Ji T, Flores-Parra I M, Yang X T, Li M. Segmentation of thermal infrared images of cucumber leaves using K-means clustering for estimating leaf wetness duration. Int J Agric & Biol Eng, 2020; 13(3): 161–167.

Image processing is an important way to obtain information and is widely used in important fields such as military, medical and transportation. Image segmentation plays an important role in image processing. In view of the inherent complexity and correlation of the image itself, how to deal with the uncertainty in the image segmentation process is the main work to obtain a more accurate image segmentation result. We propose an image single-threshold segmentation algorithm using the maximum dependency of variable precision rough set (VPRS). The algorithm uses VPRS to represent the image, and uses the maximum dependency and particle swarm optimization to solve the optimal image segmentation threshold, which effectively handles the uncertainty in image segmentation. The experiments show that the single-threshold segmentation algorithm has certain practicability and flexibility, the segmentation effect is better than the maximum average information entropy method, and the segmentation efficiency is significantly higher than the ordinary iterative method.

River water level detection is accomplished by sensors. However, the bending of the river channel, width changes, riverbed morphology and other factors result in errors in the water level images monitored by the sensors, which in turn affects the accuracy of small-scale feature mapping. For this reason, this paper proposes a river water level AI detection method based on image segmentation processing. The river water level image is preprocessed by mean filtering and histogram equalization to remove noise and enhance contrast. The Retinex defogging algorithm is utilized to further improve the image quality. An image segmentation model based on L1-[Formula: see text]L2 regularity is constructed, and the image segmentation is realized by solving DCA and ADMM algorithms. In the traditional YOLOv5 network structure, the meso-scale and large-scale feature mappings are fused into the small-scale feature mapping by weighted summation to enhance the spatial information, detail information and semantic information transfer capability of the small-scale feature mapping, and the water level detection is carried out based on the improved YOLOv5. The experimental results show that when the method in this paper deals with different backgrounds, light and reflection images, the segmented accurate distinction between the river water level region and other background regions has obvious boundaries. The accuracy of water level detection is always around 98%, and the recall rate is always above 96%, which is practical.

information from the original image as compared with crisp or hard segmentation methods. In practice, noisy images (even high noise images) are very co mmon. It's very essential and critical to deal with such images to process real-image segmentation and pattern recognition. In this paper, differences of credibilistic clustering algorithm (CCA) and fuzzy c-means algorithm (FCM) in dealing with noisy images are studied and the research shows that in most cases, CCA performs better than FCM in high noise image segmentation. Based on that, a new kind of fuzzy clustering methods is presented. It combines spatial credibilistic clustering algorithm (SCCA) with particle swarm optimization (PSO) and takes full advantages of them. The advantages that come from CCA in noise image segmentation also help in SCCA, and the imposition of spatial information enlarges the advantage. The addition of PSO helps to improve global search performance; thereby the novel methods overcome the drawback of single clustering methods - local optimal solutions. Computational experiments show that the proposed methods give the best segmentation results when compared with FCM, CCA, spatial fuzzy c-means algorithm (SFCM), SCCA and the PSO incorporated versions of FCM, CCA, and SFCM.

Medical-image segmentation is of primary importance in the development of computer assisted detection (CAD) of mammographic masses. Image segmentation is used for finding regions of interest (ROI) within mammograms that are further processed to estimate their likelihood of being a potential lesion or mass. This task can be stated as follows: €œA gray-scale image is represented as a 1D array X = (x1, x2,...,xN), where xn is an input feature for pixel n and N is the total number of pixels in the image. The input feature vector xn may be a D dimensional vector or simply the gray-scale value of the pixel n. Let the underlying true segmentation of the image be denoted as Y = (y1, y2,..., yN). It is assumed that the number of classes is predetermined as a set of known class labels I‰l, where l Iµ {1,..., L{ and therefore the class label of pixel n is indicated as y n ∈{I‰ l } L l=1 . The task is to predict the A·n estimate of the segmentation for each pixel. In an automated system, such likelihood estimates are generated using a classifier that takes as input some features that describe region properties, e.g., image texture. The segmentation of digital mammograms is a difficult task. A number of different approaches have been adopted in this area with varying success. Broadly speaking, image segmentation can be carried out either as: (1) image preprocessing step with the main objective of identifying regions of interest (spatially coherent collection of pixels within the image) and a subsequent feature extraction and classification step is required to characterize a given region of interest as either €œnormal€ or €œsuspicious€; or (2) part of the overall classification scheme, where the likelihood of each pixel being €œsuspicious is calculated to generate a probability image using pixel-based features as input to a classifier. The probability image is thereafter thresholded to give both the segmentation and classification output. The main difference between (1) and (2) is that in the first case, image segmentation is needed before classification can be performed; whereas in the second case, classification and segmentation are both based on thresholding probability estimates of pixels and are achieved at the same time. The image segmentation approaches, with the aim of finding regions of interest, were first attempted in digital mammography with varying techniques, including global thresholding, region growing, region clustering, template matching, edge operators and filtering, and edge-based hybrid methods. Image segmentation is developed from pixel-based probability estimation is underpinned by modeling features using parametric methods, Gaussian mixture modeling, Markov random fields, and other methods. A detailed discussion on mammogram-based image segmentation with their algorithms is available in Singh and Bovis. Experience shows that it is quite difficult to select an outright winning image-segmentation method, since different approaches have been shown to be suitable for different data sets and show varied performance under different experimental conditions. However, there is a consensus that image segmentation in medical imaging is most successful when explicitly using a priori information about the problem to be studied.

Objective To study the image segmentation, three-dimensional (3D) reconstruction and simulation operation of choledocholithotomy and T-tube drainage based on the computed tomagraphy (CT) data of patient with choledocholithiasis. Methods Patient with choledocholithiasis underwent 64-slice spiral CT imaging. The images segmentation and 3D reconstruction were performed by Medical Image Process System (MIPS) to construct the 3D model of the hepatobiliary system. The model was modified by FreeForm Modeling System. The virtual surgical instruments were developed by GHOST SDK software, and were imported to the virtual surgery. Results The data of plain, arterial phase, venous phase and portal venous phase scanning were collected, and the data stored in DICOM format were transformed to BMP format. A model of the hepatobiliary system was constructed after the data was segmented by MIPS, and then the model was exported in the STL format. The data in STL format were imported to FreeForm Modeling System for smoothing the model. Different structures were assigned different colors to make the model more vivid. The self-developed virtual surgical instruments were imported to the system, and the virtual surgery for choledocholithiasis was performed with PHANToM. Conclusions With the help of MIPS, the image segmentation and 3D reconstruction of the model are finished rapidly and effectively. After the virtual surgical instruments are developed in FreeForm Modeling System, the virtual surgery can be achieved in the 3D model with the assistance of PHANToM. Key words: Choledocholithiasis; Three-dimensional reconstruction; Virtual surgery

The Breast Cancer is second of the dangerous type diseases that is very effective for women in the world. Breast Cancer is spread very fast in last stage. It’s not give a more time for patients for treatment. We need accurate and efficient diagnosis system over a short period of time that can processed a large amount of data in a short time. It’s only possible using deep learning techniques. We are studying on a new diagnosis system for detecting Breast cancer in early stage. But in this paper we are describing the all techniques and images processing method for segmentation and filter images for breast cancer diagnosis. In this paper we have describe all algorithm and techniques for image pre-processing and post progressing. We have reviewed more than 20 papers from different publisher and find out that many researcher working on breast cancer diagnosis techniques. It’s a very latest and effective techniques for helping and saving the human life. In this paper we have also describe some experimental part of images segmentation and filter images with different filter method. It’s a very interesting area for researcher. In this paper we have used (Mammography Image Analysis Society) MIAS dataset having 95 images in .pgm format with 1024 X 1024 pixel. All paper divided in two main part first is data collection and image pre-processing. In this paper we have describe some most used deep learning pre-trained model that is very effective for images pre-processing. As per reviewed study the AlexNet, GoogleNet and VGG16 are provided best accuracy for detecting breast cancer in mammography images. We also used many filter method for image quality improvement. Filters are used to filter unwanted things or object in a spatial domain or surface. The main objective of the filter are to improve the quality of the images by enhancing and improve interoperability of the images. In this paper we also showing the comparison between most used pre-trained model that are used for breast cancer diagnosis.

Image segmentation is the problem of partitioning an image into different subsets, where each subset may have a different characterization in terms of color, intensity, texture, and/or other features. Segmentation is a fundamental component of image processing, and plays a significant role in computer vision, object recognition, and object tracking. Active Contour Models (ACMs) constitute a powerful energy-based minimization framework for image segmentation, which relies on the concept of contour evolution. Starting from an initial guess, the contour is evolved with the aim of approximating better and better the actual object boundary. Handling complex images in an efficient, effective, and robust way is a real challenge, especially in the presence of intensity inhomogeneity, overlap between the foreground/background intensity distributions, objects characterized by many different intensities, and/or additive noise. In this thesis, to deal with these challenges, we propose a number of image segmentation models relying on variational level set methods and specific kinds of neural networks, to handle complex images in both supervised and unsupervised ways. Experimental results demonstrate the high accuracy of the segmentation results, obtained by the proposed models on various benchmark synthetic and real images compared with state-of-the-art active contour models.

A novel and efficient algorithm for improving the efficiency of image in painting is proposed. The proposed technique separates the in painting process into four different and important steps. First, shrinking the original image to 0.02-0.25 times through the downscaling method, besides search a best-exemplar from source region in the downscaled image, get those counterpart by proposed rules from the origin image and fill whole damaged region in origin image, and then using the method of image segmentation to in painting the regions of incomplete restoration in the downscaled image. Finally, these are filled into the in painted region of the original image. The result demonstrates that this method is 100~5 times the efficiency of the method which is existed now.

This paper has considered a model of image segmentation using convolutional neural networks and studied the process efficiency based on models involving training the deep layers of convolutional neural networks. There are objective difficulties associated with determining the optimal characteristics of neural networks, so there is an issue related to retraining the neural network. Eliminating retraining by determining the optimal number of epochs only would not suffice since it does not provide high accuracy. The requirements for the set of images for training and model verification were defined. These requirements are best met by the image sets PASCAL VOC (United Kingdom) and NVIDIA-Aerial Drone (USA). It has been established that AlexNet (Canada) is a trained model and could perform image segmentation while object recognition reliability is insufficient. Therefore, there is a need to improve the efficiency of image segmentation. It is advisable to use the AlexNet architecture to build a specialized model, which, by changing the parameters and retraining some layers, would allow for a better process of image segmentation. Five models have been trained using the following parameters: learning speed, the number of epochs, optimization algorithm, the type of learning speed change, a gamma coefficient, a pre-trained model. A convolutional neural network has been developed to improve the accuracy and efficiency of image segmentation. Optimal neural network training parameters have been determined: learning speed is 0.0001, the number of epochs is 50, a gamma coefficient is 0.1, etc. An increase in accuracy by 3 % was achieved, which makes it possible to assert the correctness of the choice of the architecture for the developed network and the selection of parameters. That allows this network to be used for practical tasks related to image segmentation, in particular for devices with limited computing resources